Multi-class multi-scale stacked sequential learning

Authors:
Eloi Puertas;Sergio Escalera;Oriol Pujol
Affiliations:
Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain and Computer Vision Center, Bellaterra, Spain;Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain and Computer Vision Center, Bellaterra, Spain;Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain and Computer Vision Center, Bellaterra, Spain
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

Citing 12
Cited 1

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Selection of negative samples and two-stage combination of multiple features for action detection in thousands of videos

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Abstract

One assumption in supervised learning is that data is independent and identically distributed. However, this assumption does not hold true in many real cases. Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we revise the Multi-Scale Sequential Learning approach (MSSL) for applying it in the multi-class case (MMSSL). We have introduced the ECOC framework in the MSSL base classifiers and a formulation for calculating confidence maps from the margins of the base classifiers. Another important contribution of this papers is the MMSSL compression approach for reducing the number of features in the extended data set. The proposed methods are tested on 5-class and 9-class image databases.